import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

print('tensorflow version:' + tf.__version__)
fashion_minst = keras.datasets.fashion_mnist
(train_data, train_labels), (test_data, test_labels) = fashion_minst.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
print('训练集大小：' + str(train_data.shape))
# 数据预处理
'''
plt.figure()
plt.imshow(train_data[0])
plt.colorbar()
plt.grid(False)
plt.show()
'''
train_data = train_data / 255.0
test_data = test_data / 255.0
'''
plt.figure(figsize=(10, 10))
for i in range(25):
    plt.subplot(5, 5, i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_data[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()
'''
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_data, test_labels, verbose=2)
print('\n模型准确度:' + str(test_acc))
probablity_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probablity_model.predict(test_data)
print('最大可能是：' + str(np.argmax(predictions[0])))
print('真实是：' + str(test_labels[0]))


#数据可视化
def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array, true_label[i], img[i]
    plt.grid(True)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
        color = 'blue'
    else:
        color = 'red'

    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                         100 * np.max(predictions_array),
                                         class_names[true_label]),
               color=color)


def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color="#777777")
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)

    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')

'''
ii = 0
plt.figure(figsize=(6,3))
plt.subplot(1, 2, 1)
plot_image(ii, predictions[ii], test_labels, test_data)
plt.subplot(1, 2, 2)
plot_value_array(ii, predictions[ii],  test_labels)
plt.show()
'''

# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
  plt.subplot(num_rows, 2*num_cols, 2*i+1)
  plot_image(i, predictions[i], test_labels, test_data)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
